Use of prior knowledge to inform restoration projects in estuaries of GOM

# randomize author order
aut <- c('Marcus Beck', 'Kirsten Dorans', 'Jessica Renee Henkel', 'Kathryn Ireland', 'Ed Sherwood', 'Patricia Varela') %>% 
  sample %>% 
  paste(collapse = ', ')

cat('By', aut)

By Kirsten Dorans, Kathryn Ireland, Marcus Beck, Ed Sherwood, Jessica Renee Henkel, Patricia Varela

Graphic: eli-ocean.org

*Despite considerable investments in aquatic ecosystem restoration consistent and comprehensive effectiveness evaluation continues to elude practitioners at geographic scales. (Diefenderfer et al. 2016)

• A general and flexible framework that can be applied to unique locations and is not limited by data availability
• Explicit quantification of uncertainty and model updates with new data
• More focused restoration towards specific regional issues
• Improved ability to predict outcomes of proposed restoration projects

• “Softer” Restoration -> Local ordinances (e.g. ferilizer restrictions), Education, etc.
• “Soft” Restoration -> Habitat Creation, Enhancement and Management/Protection Measures
• “Hard” Restoration -> Stormwater BMPs, Point Source Reductions through Time, Regulations

• Can we identify a change in water quality from restoration?
• What data do we have?
• Can we plyr the data to identify a signal?
• Can we plyr the data as input to a BN?

WQ and restoration sites: Spatial match

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after, slice

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 3
# Groups:   stat [1]
   stat     cmb     cval
  <int>   <chr>    <dbl>
1     7 hab_aft 8.255185
2     7 hab_bef 8.350187
3     7 wtr_aft 8.053273
4     7 wtr_bef 8.129733

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 4
   stat     hab     wtr     cval
  <int>  <fctr>  <fctr>    <dbl>
1     7 hab_aft wtr_aft 8.154229
2     7 hab_aft wtr_bef 8.192459
3     7 hab_bef wtr_aft 8.201730
4     7 hab_bef wtr_bef 8.239960

What do the data look like? For many water quality station matched to many restoration sites…

# A tibble: 20 x 4
    stat     hab     wtr      cval
   <int>  <fctr>  <fctr>     <dbl>
 1     6 hab_aft wtr_aft  8.903273
 2     6 hab_aft wtr_bef 11.720206
 3     6 hab_bef wtr_aft 11.902951
 4     6 hab_bef wtr_bef 14.719883
 5     7 hab_aft wtr_aft  8.154229
 6     7 hab_aft wtr_bef  8.192459
 7     7 hab_bef wtr_aft  8.201730
 8     7 hab_bef wtr_bef  8.239960
 9     8 hab_aft wtr_aft 19.867100
10     8 hab_aft wtr_bef 17.444274
11     8 hab_bef wtr_aft 17.331973
12     8 hab_bef wtr_bef 14.909147
13     9 hab_aft wtr_aft  9.030021
14     9 hab_aft wtr_bef  8.621069
15     9 hab_bef wtr_aft  8.398558
16     9 hab_bef wtr_bef  7.989606
17    11 hab_aft wtr_aft  6.576058
18    11 hab_aft wtr_bef  6.727664
19    11 hab_bef wtr_aft  8.112902
20    11 hab_bef wtr_bef  8.264508

What do the data look like? For many water quality station matched to many restoration sites…

Patricia

• Specifics of BN
• Outcomes/interpretation/applications

• Next steps (all)